-
Notifications
You must be signed in to change notification settings - Fork 18
/
openvino-usbcamera-cpu-ncs2-sync.py
executable file
·265 lines (213 loc) · 9.72 KB
/
openvino-usbcamera-cpu-ncs2-sync.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
import sys
import os
import numpy as np
import cv2
from os import system
import io
import time
from os.path import isfile
from os.path import join
import re
import argparse
import platform
try:
from armv7l.openvino.inference_engine import IENetwork, IEPlugin
except:
from openvino.inference_engine import IENetwork, IEPlugin
def getKeypoints(probMap, threshold=0.1):
mapSmooth = cv2.GaussianBlur(probMap, (3, 3), 0, 0)
mapMask = np.uint8(mapSmooth>threshold)
keypoints = []
contours = None
try:
#OpenCV4.x
contours, _ = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
except:
#OpenCV3.x
_, contours, _ = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
blobMask = np.zeros(mapMask.shape)
blobMask = cv2.fillConvexPoly(blobMask, cnt, 1)
maskedProbMap = mapSmooth * blobMask
_, maxVal, _, maxLoc = cv2.minMaxLoc(maskedProbMap)
keypoints.append(maxLoc + (probMap[maxLoc[1], maxLoc[0]],))
return keypoints
def getValidPairs(outputs, w, h):
valid_pairs = []
invalid_pairs = []
n_interp_samples = 10
paf_score_th = 0.1
conf_th = 0.7
for k in range(len(mapIdx)):
pafA = outputs[0, mapIdx[k][0], :, :]
pafB = outputs[0, mapIdx[k][1], :, :]
pafA = cv2.resize(pafA, (w, h))
pafB = cv2.resize(pafB, (w, h))
candA = detected_keypoints[POSE_PAIRS[k][0]]
candB = detected_keypoints[POSE_PAIRS[k][1]]
nA = len(candA)
nB = len(candB)
if( nA != 0 and nB != 0):
valid_pair = np.zeros((0,3))
for i in range(nA):
max_j=-1
maxScore = -1
found = 0
for j in range(nB):
d_ij = np.subtract(candB[j][:2], candA[i][:2])
norm = np.linalg.norm(d_ij)
if norm:
d_ij = d_ij / norm
else:
continue
interp_coord = list(zip(np.linspace(candA[i][0], candB[j][0], num=n_interp_samples),
np.linspace(candA[i][1], candB[j][1], num=n_interp_samples)))
paf_interp = []
for k in range(len(interp_coord)):
paf_interp.append([pafA[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))],
pafB[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))] ])
paf_scores = np.dot(paf_interp, d_ij)
avg_paf_score = sum(paf_scores)/len(paf_scores)
if ( len(np.where(paf_scores > paf_score_th)[0]) / n_interp_samples ) > conf_th :
if avg_paf_score > maxScore:
max_j = j
maxScore = avg_paf_score
found = 1
if found:
valid_pair = np.append(valid_pair, [[candA[i][3], candB[max_j][3], maxScore]], axis=0)
valid_pairs.append(valid_pair)
else:
invalid_pairs.append(k)
valid_pairs.append([])
return valid_pairs, invalid_pairs
def getPersonwiseKeypoints(valid_pairs, invalid_pairs):
personwiseKeypoints = -1 * np.ones((0, 19))
for k in range(len(mapIdx)):
if k not in invalid_pairs:
partAs = valid_pairs[k][:,0]
partBs = valid_pairs[k][:,1]
indexA, indexB = np.array(POSE_PAIRS[k])
for i in range(len(valid_pairs[k])):
found = 0
person_idx = -1
for j in range(len(personwiseKeypoints)):
if personwiseKeypoints[j][indexA] == partAs[i]:
person_idx = j
found = 1
break
if found:
personwiseKeypoints[person_idx][indexB] = partBs[i]
personwiseKeypoints[person_idx][-1] += keypoints_list[partBs[i].astype(int), 2] + valid_pairs[k][i][2]
elif not found and k < 17:
row = -1 * np.ones(19)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
row[-1] = sum(keypoints_list[valid_pairs[k][i,:2].astype(int), 2]) + valid_pairs[k][i][2]
personwiseKeypoints = np.vstack([personwiseKeypoints, row])
return personwiseKeypoints
fps = ""
detectfps = ""
framecount = 0
time1 = 0
camera_width = 320
camera_height = 240
keypointsMapping = ['Nose', 'Neck', 'R-Sho', 'R-Elb', 'R-Wr', 'L-Sho', 'L-Elb', 'L-Wr', 'R-Hip', 'R-Knee', 'R-Ank', 'L-Hip', 'L-Knee', 'L-Ank', 'R-Eye', 'L-Eye', 'R-Ear', 'L-Ear']
POSE_PAIRS = [[1,2], [1,5], [2,3], [3,4], [5,6], [6,7], [1,8], [8,9], [9,10], [1,11], [11,12], [12,13], [1,0], [0,14], [14,16], [0,15], [15,17], [2,17], [5,16]]
mapIdx = [[31,32], [39,40], [33,34], [35,36], [41,42], [43,44], [19,20], [21,22], [23,24], [25,26], [27,28], [29,30], [47,48], [49,50], [53,54], [51,52], [55,56], [37,38], [45,46]]
colors = [[0,100,255], [0,100,255], [0,255,255], [0,100,255], [0,255,255], [0,100,255], [0,255,0], [255,200,100], [255,0,255], [0,255,0], [255,200,100], [255,0,255], [0,0,255], [255,0,0], [200,200,0], [255,0,0], [200,200,0], [0,0,0]]
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 30)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--device", help="Specify the target device to infer on; CPU, GPU, MYRIAD is acceptable. (Default=CPU)", default="CPU", type=str)
parser.add_argument("-b", "--boost", help="Setting it to True will make it run faster instead of sacrificing accuracy. (Default=False)", default=False, type=bool)
args = parser.parse_args()
plugin = IEPlugin(device=args.device)
if "CPU" == args.device:
if platform.processor() == "x86_64":
plugin.add_cpu_extension("lib/libcpu_extension.so")
if args.boost == False:
model_xml = "models/train/test/openvino/mobilenet_v2_1.4_224/FP32/frozen-model.xml"
else:
model_xml = "models/train/test/openvino/mobilenet_v2_0.5_224/FP32/frozen-model.xml"
elif "GPU" == args.device or "MYRIAD" == args.device:
if args.boost == False:
model_xml = "models/train/test/openvino/mobilenet_v2_1.4_224/FP16/frozen-model.xml"
else:
model_xml = "models/train/test/openvino/mobilenet_v2_0.5_224/FP16/frozen-model.xml"
else:
print("Specify the target device to infer on; CPU, GPU, MYRIAD is acceptable.")
sys.exit(0)
model_bin = os.path.splitext(model_xml)[0] + ".bin"
net = IENetwork(model=model_xml, weights=model_bin)
input_blob = next(iter(net.inputs))
exec_net = plugin.load(network=net)
inputs = net.inputs["image"]
h = inputs.shape[2] #368
w = inputs.shape[3] #432
threshold = 0.1
nPoints = 18
try:
while True:
t1 = time.perf_counter()
ret, color_image = cap.read()
if not ret:
break
colw = color_image.shape[1]
colh = color_image.shape[0]
new_w = int(colw * min(w/colw, h/colh))
new_h = int(colh * min(w/colw, h/colh))
resized_image = cv2.resize(color_image, (new_w, new_h), interpolation = cv2.INTER_CUBIC)
canvas = np.full((h, w, 3), 128)
canvas[(h - new_h)//2:(h - new_h)//2 + new_h,(w - new_w)//2:(w - new_w)//2 + new_w, :] = resized_image
prepimg = canvas
prepimg = prepimg[np.newaxis, :, :, :] # Batch size axis add
prepimg = prepimg.transpose((0, 3, 1, 2)) # NHWC to NCHW, (1, 3, 368, 432)
outputs = exec_net.infer(inputs={input_blob: prepimg})["Openpose/concat_stage7"]
detected_keypoints = []
keypoints_list = np.zeros((0, 3))
keypoint_id = 0
for part in range(nPoints):
probMap = outputs[0, part, :, :]
probMap = cv2.resize(probMap, (canvas.shape[1], canvas.shape[0])) # (432, 368)
keypoints = getKeypoints(probMap, threshold)
keypoints_with_id = []
for i in range(len(keypoints)):
keypoints_with_id.append(keypoints[i] + (keypoint_id,))
keypoints_list = np.vstack([keypoints_list, keypoints[i]])
keypoint_id += 1
detected_keypoints.append(keypoints_with_id)
frameClone = np.uint8(canvas.copy())
for i in range(nPoints):
for j in range(len(detected_keypoints[i])):
cv2.circle(frameClone, detected_keypoints[i][j][0:2], 5, colors[i], -1, cv2.LINE_AA)
valid_pairs, invalid_pairs = getValidPairs(outputs, w, h)
personwiseKeypoints = getPersonwiseKeypoints(valid_pairs, invalid_pairs)
for i in range(17):
for n in range(len(personwiseKeypoints)):
index = personwiseKeypoints[n][np.array(POSE_PAIRS[i])]
if -1 in index:
continue
B = np.int32(keypoints_list[index.astype(int), 0])
A = np.int32(keypoints_list[index.astype(int), 1])
cv2.line(frameClone, (B[0], A[0]), (B[1], A[1]), colors[i], 3, cv2.LINE_AA)
cv2.putText(frameClone, fps, (w-170,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
cv2.namedWindow("USB Camera", cv2.WINDOW_AUTOSIZE)
cv2.imshow("USB Camera" , frameClone)
if cv2.waitKey(1)&0xFF == ord('q'):
break
# FPS calculation
framecount += 1
if framecount >= 15:
fps = "(Playback) {:.1f} FPS".format(time1/15)
framecount = 0
time1 = 0
t2 = time.perf_counter()
elapsedTime = t2-t1
time1 += 1/elapsedTime
except:
import traceback
traceback.print_exc()
finally:
print("\n\nFinished\n\n")